{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# YData Synthetic and Great Expectations: A Data Expectations Framework for a Synthetic Data Engine\n",
    "\n",
    "This notebook is a step-by-step tutorial on how you can inetgrate the great expectations framework while working on a ydata-synthetic project.\n",
    "Specifically we will\n",
    "1. Setup the project structure by initializing a Data Context\n",
    "2. Download/Extract the real data set which we use to create synthetic data\n",
    "3. Configure a Data Source to connect our data\n",
    "4. Create an Expectation Suite using the built-in Great Expectations profiler\n",
    "5. Transform the real data for modeling\n",
    "6. Train the synthesizers and create the model file\n",
    "7. Sample synthetic data from synthesizer\n",
    "8. Inverse transform the data to obtain the original format\n",
    "9. Create a new checkpoint to validate the synthetic data against the real data\n",
    "10. Evaluate the synthetic data using Data Docs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But before we get started, ensure that you have installed both ydata-synthetic and great_expectations after creating a virtual environment.\n",
    "\n",
    "!pip intsall ydata-synthetic great_expetctaions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Setup the project structure through a Data Context\n",
    "\n",
    "In Great Expectations, your Data Context manages the project configuration. There are multiple ways to create the Data Context, however the simplest one is by using the CLI that comes along when you install the great_expectations package.\n",
    "\n",
    "Note: For this tutorial we'll use the newest V3 (Batch request) API so youd see the flag ```--v3-api``` in most of the commands.\n",
    "\n",
    "Open your terminal and navigate to the project directory and type in the following:\n",
    "\n",
    "```great_expectations --v3-api init```\n",
    "\n",
    "Press enter to complete the creation of the Data Context and that's about it.\n",
    "\n",
    "If you're curious about the modified project structure, here's an excerpt from the GE documentations:\n",
    "- great_expectations.yml contains the main configuration of your deployment.\n",
    "- The expectations/ directory stores all your Expectations as JSON files. If you want to store them somewhere else, you can change that later.\n",
    "- The plugins/ directory holds code for any custom plugins you develop as part of your deployment.\n",
    "- The uncommitted/ directory contains files that shouldn’t live in version control. It has a .gitignore configured to exclude all its contents from version control. The main contents of the directory are:\n",
    "    - uncommitted/config_variables.yml, which holds sensitive information, such as database credentials and other secrets.\n",
    "    - uncommitted/documentation, which contains Data Docs generated from Expectations, Validation Results, and other metadata.\n",
    "    - uncommitted/validations, which holds Validation Results generated by Great Expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AnCU8-Mal4fV",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Step 2: Download/Extract the real data set which we use to create synthetic data\n",
    "\n",
    "To move forward, we pick an use-case example of \"The Credit Card Fraud Dataset - Synthesizing the Minority Class\" where we aim to synthesize the minority class of the credit card fraud dataset that has a high imbalance.Further a practical exercise is presented to showcase the usage of the YData Synthetic library along with\n",
    "GANs to synthesize tabular data. For the purpose of this exercise, dataset of credit card fraud from Kaggle is used, that can be found here:\n",
    "https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
    "\n",
    "Since we're interested only on the fraud class data points of the dataset, let's filter them and write it to teh data directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all required libraries\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import sklearn.cluster as cluster\n",
    "\n",
    "# from ydata_synthetic.synthesizers.regular import VanilllaGAN\n",
    "from ydata_synthetic.synthesizers.regular import WGAN_GP\n",
    "from ydata_synthetic.synthesizers import ModelParameters, TrainParameters\n",
    "from ydata_synthetic.preprocessing.regular.credit_fraud import *\n",
    "from ydata_synthetic.postprocessing.regular.inverse_preprocesser import inverse_transform\n",
    "\n",
    "model = WGAN_GP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 31)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the original data\n",
    "data = pd.read_csv('./data/creditcard.csv')\n",
    "\n",
    "#Filter the minority class\n",
    "train_data = data.loc[ data['Class']==1 ].copy()\n",
    "\n",
    "# Inspect the shape of the data\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to the data folder\n",
    "train_data.to_csv('./data/creditcard_fraud.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Configure a Data Source to connect our data\n",
    "\n",
    "In Great Expectations, Datasources simplify connections, by managing configuration and providing a consistent, cross-platform API for referencing data.\n",
    "\n",
    "Let’s configure our first Datasource: a connection to the data directory we’ve provided in the repo. Instead, this could even be a data base connection, and more.\n",
    "\n",
    "```great_expectations --v3-api datasource new```\n",
    "\n",
    "You would be presented with different options, select:\n",
    "- 1. Files on a filesystem (for processing with Pandas or Spark) \n",
    "\n",
    "and for processing the data select\n",
    "\n",
    "\n",
    "-  1. Pandas\n",
    "\n",
    "and finally enter the directory as: data (where we have our real data)\n",
    "\n",
    "Once you've entered the details an jupyter notebook will open up. This is just the way Great Expectations has given templated codes, which helps us create expectations with a few code changes.\n",
    "\n",
    "Let's change the Datasource name to something more specific.\n",
    "\n",
    "Edit the second code cell as follows:\n",
    "```datasource_name = \"data__dir\"```\n",
    "\n",
    "Then execute all cells in the notebook in order to save the new Datasource. If successful, the last cell will print a list of all Datasources, including the one you just created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Create an Expectation Suite using the built-in Great Expectations profiler\n",
    "\n",
    "An expectation is nothing but a falsifiable, verifiable statement about data. Expectations provide a language to talk about data characteristics and data quality - humans to humans, humans to machines, and machines to machines.\n",
    "\n",
    "The idea here is that we assume that the real data has the ideal quality of the data we want to be synthesized, so we use the real data to create a set of expectations which we can later use to evaluate our synthetic data.\n",
    "\n",
    "The CLI will help create our first Expectation Suite. Suites are simply collections of Expectations. We can use the built-in profiler to automatically create an Expectation Suite called `creditcard.quality`\n",
    "\n",
    "Type the following into your terminal:\n",
    "```great_expectations --v3-api suite new```\n",
    "\n",
    "You would be presented with a few options on the terminal. Choose 3 to create the Expectation Suite Automatically, using a profiler, and 1 to profile the real fraud dataset creditcard_fraud.csv which we saved in step 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step might be a bit confusing in the beginning but stay with us. Again another jupyter notebook would be opened with boilerplate code for creating a new expectation suite. The code is pretty standard, however, please note the second cell, all columns are added to the list of ignored columns. In our example we want to validate every single column, hence remove (or comment) out the columns from the ignored_columns list.\n",
    "\n",
    "Other than that, go ahead and execute the entire notebook. This will create an expectation suite against the real credit card fraud dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Transform the real data for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created the expectation suite, we shift our focus back to creating the synthetic data. We follow the standard process of transforming the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3o4V8-ypl4fa",
    "outputId": "39fabdb7-b3e4-492f-85f0-cd6232b45609",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset info: Number of records - 492 Number of variables - 31\n",
      "   count\n",
      "0    384\n",
      "1    108\n"
     ]
    }
   ],
   "source": [
    "# Extract list of columns\n",
    "data_cols = list(data.columns[ data.columns != 'Class' ])\n",
    "\n",
    "# Before training the GAN do not forget to apply the required data transformations\n",
    "# To ease here we've applied a PowerTransformation - make data distribution more Gaussian-like.\n",
    "_, data, preprocessor = transformations(data)\n",
    "\n",
    "# For the purpose of this example we will only synthesize the minority class\n",
    "# train_data contains 492 rows which had 'Class' value as 1 (which were very few)\n",
    "train_data = data.loc[ data['Class']==1 ].copy()\n",
    "\n",
    "print(\"Dataset info: Number of records - {} Number of variables - {}\".format(train_data.shape[0], train_data.shape[1]))\n",
    "\n",
    "# We define a K-means clustering method using sklearn, and declare that\n",
    "# we want 2 clusters. We then apply this algorithm (fit_predict) to our train_data\n",
    "# We essentially get an array of 492 rows ('labels') having values either 0 or 1 for the 2 clustered classes.\n",
    "algorithm = cluster.KMeans\n",
    "args, kwds = (), {'n_clusters':2, 'random_state':0}\n",
    "labels = algorithm(*args, **kwds).fit_predict(train_data[ data_cols ])\n",
    "\n",
    "# Get the count of both classes\n",
    "print( pd.DataFrame( [ [np.sum(labels==i)] for i in np.unique(labels) ], columns=['count'], index=np.unique(labels) ) )\n",
    "\n",
    "# Assign the k-means clustered classes' labels to the a seperate copy of train data 'fraud_w_classes'\n",
    "fraud_w_classes = train_data.copy()\n",
    "fraud_w_classes['Class'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 31)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ezlIjKbl4fb",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Step 6: Train the synthesizers and create the model\n",
    "\n",
    "Below you can try to train your own generators using the available GANs architectures. You can train it either with labels (created using KMeans) or with no labels at all. \n",
    "\n",
    "Remember that for this exercise in particular we've decided to synthesize only the minority class from the Credit Fraud dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "7FMDs5eql4fb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_sample.columns:\n",
      "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Define the GAN and training parameters\n",
    "noise_dim = 32\n",
    "dim = 128\n",
    "batch_size = 128\n",
    "\n",
    "log_step = 100\n",
    "epochs = 500\n",
    "learning_rate = 5e-4\n",
    "beta_1 = 0.5\n",
    "beta_2 = 0.9\n",
    "models_dir = './cache'\n",
    "\n",
    "train_sample = fraud_w_classes.copy().reset_index(drop=True)\n",
    "print(\"train_sample.columns:\")\n",
    "print(train_sample.columns)\n",
    "\n",
    "# There's only 1 class, so essentially rename the 'Class' to 'Class_1',\n",
    "# which tells weather a sample data is of class 1 or not.\n",
    "train_sample = pd.get_dummies(train_sample, columns=['Class'], prefix='Class', drop_first=True)\n",
    "\n",
    "# 'Class_1' label\n",
    "label_cols = [ i for i in train_sample.columns if 'Class' in i ]\n",
    "\n",
    "# All columns except 'Class_1'\n",
    "data_cols = [ i for i in train_sample.columns if i not in label_cols ]\n",
    "\n",
    "# Scale down the data, and rename it to 'train_no_label'\n",
    "train_sample[ data_cols ] = train_sample[ data_cols ] / 10 # scale to random noise size, one less thing to learn\n",
    "train_no_label = train_sample[ data_cols ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Setting the GAN model parameters and the training step parameters\n",
    "gan_args = ModelParameters(batch_size=batch_size,\n",
    "                           lr=learning_rate,\n",
    "                           betas=(beta_1, beta_2),\n",
    "                           noise_dim=noise_dim,\n",
    "                           n_cols=train_sample.shape[1],\n",
    "                           layers_dim=dim)\n",
    "\n",
    "train_args = TrainParameters(epochs=epochs,\n",
    "                             sample_interval=log_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qgMDmyall4fc",
    "outputId": "ae669bdf-01b6-49d9-a254-cc0776508f7b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▋                                                                                 | 4/500 [00:01<01:47,  4.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | disc_loss: 1.3900643587112427 | gen_loss: 0.0045707011595368385\n",
      "Epoch: 1 | disc_loss: 0.08449796587228775 | gen_loss: 0.031327493488788605\n",
      "Epoch: 2 | disc_loss: -0.040593791753053665 | gen_loss: 0.07145829498767853\n",
      "Epoch: 3 | disc_loss: -0.009611748158931732 | gen_loss: 0.06341273337602615\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  1%|▉                                                                                 | 6/500 [00:01<01:11,  6.91it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4 | disc_loss: -0.03444388508796692 | gen_loss: 0.05271586403250694\n",
      "Epoch: 5 | disc_loss: 0.013021789491176605 | gen_loss: 0.044850774109363556\n",
      "Epoch: 6 | disc_loss: -0.038972511887550354 | gen_loss: 0.028071638196706772\n",
      "Epoch: 7 | disc_loss: 0.11931518465280533 | gen_loss: 0.04252813011407852\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  2%|█▉                                                                               | 12/500 [00:01<00:36, 13.24it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8 | disc_loss: -0.05106820538640022 | gen_loss: 0.02461901679635048\n",
      "Epoch: 9 | disc_loss: -0.04332617670297623 | gen_loss: 0.034593820571899414\n",
      "Epoch: 10 | disc_loss: -0.01564370095729828 | gen_loss: 0.02591433934867382\n",
      "Epoch: 11 | disc_loss: 0.01709357649087906 | gen_loss: 0.04142738878726959\n",
      "Epoch: 12 | disc_loss: -0.04015436768531799 | gen_loss: 0.0537247397005558\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  3%|██▌                                                                              | 16/500 [00:01<00:30, 15.65it/s]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 13 | disc_loss: -0.04551912471652031 | gen_loss: 0.04289420321583748\n",
      "Epoch: 14 | disc_loss: -0.020627988502383232 | gen_loss: 0.027804356068372726\n",
      "Epoch: 15 | disc_loss: -0.035393811762332916 | gen_loss: 0.0353398472070694\n",
      "Epoch: 16 | disc_loss: -0.01410762220621109 | gen_loss: 0.040031950920820236\n",
      "Epoch: 17 | disc_loss: 0.04227292910218239 | gen_loss: 0.02096324972808361\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|███▍                                                                             | 21/500 [00:01<00:27, 17.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18 | disc_loss: -0.005715332925319672 | gen_loss: 0.023008381947875023\n",
      "Epoch: 19 | disc_loss: -0.025236766785383224 | gen_loss: 0.03550225868821144\n",
      "Epoch: 20 | disc_loss: 0.015830498188734055 | gen_loss: 0.010646125301718712\n",
      "Epoch: 21 | disc_loss: -0.048145584762096405 | gen_loss: 0.027631882578134537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|████                                                                             | 25/500 [00:02<00:27, 17.17it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22 | disc_loss: -0.025476321578025818 | gen_loss: 0.025138292461633682\n",
      "Epoch: 23 | disc_loss: -0.0412670262157917 | gen_loss: 0.03930767625570297\n",
      "Epoch: 24 | disc_loss: -0.02833767980337143 | gen_loss: 0.029343927279114723\n",
      "Epoch: 25 | disc_loss: -0.040899720042943954 | gen_loss: 0.022084400057792664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|████▋                                                                            | 29/500 [00:02<00:28, 16.72it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26 | disc_loss: 0.01860101893544197 | gen_loss: 0.010496582835912704\n",
      "Epoch: 27 | disc_loss: -0.038864027708768845 | gen_loss: 0.025830868631601334\n",
      "Epoch: 28 | disc_loss: -0.029303040355443954 | gen_loss: 0.01837087795138359\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|█████▎                                                                           | 33/500 [00:02<00:28, 16.48it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 29 | disc_loss: -0.007753364741802216 | gen_loss: 0.006764734163880348\n",
      "Epoch: 30 | disc_loss: 0.09653337299823761 | gen_loss: 0.027507780119776726\n",
      "Epoch: 31 | disc_loss: 0.0024600625038146973 | gen_loss: 0.05032439902424812\n",
      "Epoch: 32 | disc_loss: -0.0213189497590065 | gen_loss: 0.019658003002405167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|█████▉                                                                           | 37/500 [00:02<00:26, 17.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 33 | disc_loss: -0.023201359435915947 | gen_loss: 0.03771991282701492\n",
      "Epoch: 34 | disc_loss: -0.047666072845458984 | gen_loss: 0.03465753048658371\n",
      "Epoch: 35 | disc_loss: -0.012007556855678558 | gen_loss: 0.031026849523186684\n",
      "Epoch: 36 | disc_loss: 0.1009155660867691 | gen_loss: 0.04273161292076111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|██████▊                                                                          | 42/500 [00:03<00:23, 19.09it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 37 | disc_loss: -0.012203656136989594 | gen_loss: 0.056589946150779724\n",
      "Epoch: 38 | disc_loss: -0.039800260215997696 | gen_loss: 0.05520864576101303\n",
      "Epoch: 39 | disc_loss: 0.06932762265205383 | gen_loss: 0.08032719790935516\n",
      "Epoch: 40 | disc_loss: 0.01195315271615982 | gen_loss: 0.05494911968708038\n",
      "Epoch: 41 | disc_loss: -0.02306850254535675 | gen_loss: 0.07383416593074799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|███████▎                                                                         | 45/500 [00:03<00:22, 20.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 42 | disc_loss: -0.02754881978034973 | gen_loss: 0.06439406424760818\n",
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      " 10%|████████▎                                                                        | 51/500 [00:03<00:22, 19.85it/s]"
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      "Epoch: 47 | disc_loss: -0.03177665174007416 | gen_loss: 0.07883434742689133\n",
      "Epoch: 48 | disc_loss: -0.012177154421806335 | gen_loss: 0.08374660462141037\n",
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      "Epoch: 52 | disc_loss: 0.04470663517713547 | gen_loss: 0.08835730701684952\n",
      "Epoch: 53 | disc_loss: -0.0400683730840683 | gen_loss: 0.06857961416244507\n",
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      " 12%|█████████▉                                                                       | 61/500 [00:04<00:24, 18.18it/s]"
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      "Epoch: 57 | disc_loss: -0.027746066451072693 | gen_loss: 0.07306496798992157\n",
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      " 13%|██████████▏                                                                      | 63/500 [00:04<00:23, 18.44it/s]"
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      "Epoch: 61 | disc_loss: -0.03720521926879883 | gen_loss: 0.11803966760635376\n",
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      " 14%|███████████                                                                      | 68/500 [00:04<00:23, 18.66it/s]"
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      "Epoch: 65 | disc_loss: -0.03501776605844498 | gen_loss: 0.1075054332613945\n",
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      " 15%|███████████▉                                                                     | 74/500 [00:04<00:21, 20.10it/s]"
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      "Epoch: 70 | disc_loss: 0.10718084126710892 | gen_loss: 0.11094164848327637\n",
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      " 16%|████████████▉                                                                    | 80/500 [00:05<00:20, 20.70it/s]"
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      "Epoch: 75 | disc_loss: -0.03155236691236496 | gen_loss: 0.08967158198356628\n",
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      " 17%|█████████████▍                                                                   | 83/500 [00:05<00:19, 21.15it/s]"
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      "Epoch: 80 | disc_loss: 0.0027820486575365067 | gen_loss: 0.0960196927189827\n",
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      " 18%|██████████████▍                                                                  | 89/500 [00:05<00:18, 21.75it/s]"
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      "Epoch: 85 | disc_loss: -0.026955053210258484 | gen_loss: 0.10890430212020874\n",
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      " 19%|███████████████▍                                                                 | 95/500 [00:05<00:18, 22.11it/s]"
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      "Epoch: 90 | disc_loss: -0.02580714225769043 | gen_loss: 0.11645698547363281\n",
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      " 20%|███████████████▉                                                                 | 98/500 [00:05<00:18, 21.73it/s]"
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      "Epoch: 95 | disc_loss: -0.010672204196453094 | gen_loss: 0.10965050011873245\n",
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      " 20%|████████████████▏                                                               | 101/500 [00:06<00:20, 19.47it/s]"
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      "Epoch: 99 | disc_loss: -0.0008646901696920395 | gen_loss: 0.09428690373897552\n",
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      " 21%|█████████████████                                                               | 107/500 [00:06<00:19, 19.82it/s]"
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      "Epoch: 103 | disc_loss: -0.01900278404355049 | gen_loss: 0.101561538875103\n",
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      " 22%|█████████████████▉                                                              | 112/500 [00:06<00:19, 19.53it/s]"
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      "Epoch: 108 | disc_loss: -0.03139788657426834 | gen_loss: 0.0986689031124115\n",
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      " 24%|██████████████████▉                                                             | 118/500 [00:06<00:18, 20.13it/s]"
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      " 24%|███████████████████▎                                                            | 121/500 [00:07<00:19, 19.48it/s]"
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      "Epoch: 118 | disc_loss: -0.02625676617026329 | gen_loss: 0.09996026754379272\n",
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      " 25%|████████████████████▎                                                           | 127/500 [00:07<00:18, 20.39it/s]"
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      "Epoch: 122 | disc_loss: -0.0247872956097126 | gen_loss: 0.08834986388683319\n",
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      " 26%|████████████████████▊                                                           | 130/500 [00:07<00:17, 20.66it/s]"
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      "Epoch: 127 | disc_loss: -0.032182879745960236 | gen_loss: 0.10441380739212036\n",
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      " 27%|█████████████████████▊                                                          | 136/500 [00:07<00:17, 20.69it/s]"
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      " 28%|██████████████████████▋                                                         | 142/500 [00:08<00:17, 20.73it/s]"
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      "Epoch: 137 | disc_loss: 0.09968604147434235 | gen_loss: 0.08269800990819931\n",
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      " 30%|████████████████████████▏                                                       | 151/500 [00:08<00:16, 21.10it/s]"
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      " 31%|█████████████████████████                                                       | 157/500 [00:08<00:15, 21.56it/s]"
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      " 32%|█████████████████████████▌                                                      | 160/500 [00:08<00:15, 21.57it/s]"
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      "Epoch: 458 | disc_loss: -0.008526436984539032 | gen_loss: 0.028112271800637245\n",
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      "Epoch: 468 | disc_loss: -0.03321819007396698 | gen_loss: 0.030527988448739052\n",
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      "Epoch: 473 | disc_loss: -0.027053065598011017 | gen_loss: 0.03010450303554535\n",
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      "Epoch: 478 | disc_loss: -0.007806470617651939 | gen_loss: 0.023931248113512993\n",
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      "Epoch: 483 | disc_loss: -0.03166632726788521 | gen_loss: 0.02713354118168354\n",
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      "Epoch: 496 | disc_loss: -0.028559289872646332 | gen_loss: 0.023987211287021637\n"
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      "Epoch: 497 | disc_loss: -0.02947324700653553 | gen_loss: 0.020921774208545685\n",
      "Epoch: 498 | disc_loss: -0.02384195849299431 | gen_loss: 0.015820099040865898\n",
      "Epoch: 499 | disc_loss: -0.030862359330058098 | gen_loss: 0.022133558988571167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Training the GAN model chosen: Vanilla GAN, CGAN, DCGAN, etc.\n",
    "synthesizer = model(gan_args, n_critic=2)\n",
    "synthesizer.train(train_sample, train_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tDjYWJPyl4fc",
    "outputId": "8a5c7afb-74ee-44ee-8902-048250d04061"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(128, 32)]               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (128, 128)                4224      \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (128, 256)                33024     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (128, 512)                131584    \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (128, 31)                 15903     \n",
      "=================================================================\n",
      "Total params: 184,735\n",
      "Trainable params: 184,735\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Generator description\n",
    "synthesizer.generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can easily save the trained generator and loaded it afterwards\n",
    "if not os.path.exists(\"./saved/gan\"):\n",
    "    os.makedirs(\"./saved/gan\")\n",
    "synthesizer.save(path=\"./saved/gan/generator_fraud.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {'GAN': ['GAN', False, synthesizer.generator]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Sample synthetic data from the Synthesizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Synthetic data generation: 100%|████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 333.37it/s]\n"
     ]
    }
   ],
   "source": [
    "# use the same shape as the real data\n",
    "\n",
    "synthetic_fraud = synthesizer.sample(492)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.014490</td>\n",
       "      <td>-0.036139</td>\n",
       "      <td>0.043463</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.099609</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>-0.027526</td>\n",
       "      <td>0.014284</td>\n",
       "      <td>0.008115</td>\n",
       "      <td>-0.061493</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020599</td>\n",
       "      <td>-0.008189</td>\n",
       "      <td>-0.032885</td>\n",
       "      <td>0.029525</td>\n",
       "      <td>-0.026076</td>\n",
       "      <td>-0.022453</td>\n",
       "      <td>0.016560</td>\n",
       "      <td>0.005409</td>\n",
       "      <td>-0.032490</td>\n",
       "      <td>0.034485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.008996</td>\n",
       "      <td>-0.083212</td>\n",
       "      <td>-0.016997</td>\n",
       "      <td>-0.003239</td>\n",
       "      <td>0.045261</td>\n",
       "      <td>-0.037142</td>\n",
       "      <td>-0.020836</td>\n",
       "      <td>0.043254</td>\n",
       "      <td>-0.016691</td>\n",
       "      <td>-0.029453</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038687</td>\n",
       "      <td>-0.031577</td>\n",
       "      <td>-0.074655</td>\n",
       "      <td>0.044621</td>\n",
       "      <td>-0.034896</td>\n",
       "      <td>-0.088456</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.020588</td>\n",
       "      <td>0.057668</td>\n",
       "      <td>0.009820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.025750</td>\n",
       "      <td>-0.004508</td>\n",
       "      <td>0.047271</td>\n",
       "      <td>0.024485</td>\n",
       "      <td>0.190426</td>\n",
       "      <td>-0.007037</td>\n",
       "      <td>0.008851</td>\n",
       "      <td>-0.017983</td>\n",
       "      <td>0.023197</td>\n",
       "      <td>-0.062995</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013625</td>\n",
       "      <td>0.014667</td>\n",
       "      <td>-0.033714</td>\n",
       "      <td>0.054258</td>\n",
       "      <td>-0.052116</td>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.021749</td>\n",
       "      <td>0.002647</td>\n",
       "      <td>-0.094893</td>\n",
       "      <td>0.016705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.027641</td>\n",
       "      <td>0.014891</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>-0.042068</td>\n",
       "      <td>0.140646</td>\n",
       "      <td>0.045977</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>0.013273</td>\n",
       "      <td>-0.066790</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023730</td>\n",
       "      <td>-0.027800</td>\n",
       "      <td>-0.028096</td>\n",
       "      <td>0.013922</td>\n",
       "      <td>-0.036212</td>\n",
       "      <td>-0.021916</td>\n",
       "      <td>0.034325</td>\n",
       "      <td>-0.011777</td>\n",
       "      <td>-0.036685</td>\n",
       "      <td>0.017355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.091014</td>\n",
       "      <td>-0.018901</td>\n",
       "      <td>0.128112</td>\n",
       "      <td>-0.025454</td>\n",
       "      <td>0.170226</td>\n",
       "      <td>-0.062996</td>\n",
       "      <td>-0.014986</td>\n",
       "      <td>-0.137715</td>\n",
       "      <td>0.033653</td>\n",
       "      <td>-0.142593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033119</td>\n",
       "      <td>0.026908</td>\n",
       "      <td>-0.060136</td>\n",
       "      <td>0.026790</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.025039</td>\n",
       "      <td>0.100087</td>\n",
       "      <td>0.054306</td>\n",
       "      <td>-0.184202</td>\n",
       "      <td>0.027151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.014490 -0.036139  0.043463  0.004229  0.099609  0.006384 -0.027526   \n",
       "1 -0.008996 -0.083212 -0.016997 -0.003239  0.045261 -0.037142 -0.020836   \n",
       "2 -0.025750 -0.004508  0.047271  0.024485  0.190426 -0.007037  0.008851   \n",
       "3  0.027641  0.014891  0.036242 -0.042068  0.140646  0.045977 -0.004796   \n",
       "4 -0.091014 -0.018901  0.128112 -0.025454  0.170226 -0.062996 -0.014986   \n",
       "\n",
       "         7         8         9   ...        21        22        23        24  \\\n",
       "0  0.014284  0.008115 -0.061493  ... -0.020599 -0.008189 -0.032885  0.029525   \n",
       "1  0.043254 -0.016691 -0.029453  ... -0.038687 -0.031577 -0.074655  0.044621   \n",
       "2 -0.017983  0.023197 -0.062995  ... -0.013625  0.014667 -0.033714  0.054258   \n",
       "3  0.011349  0.013273 -0.066790  ... -0.023730 -0.027800 -0.028096  0.013922   \n",
       "4 -0.137715  0.033653 -0.142593  ...  0.033119  0.026908 -0.060136  0.026790   \n",
       "\n",
       "         25        26        27        28        29        30  \n",
       "0 -0.026076 -0.022453  0.016560  0.005409 -0.032490  0.034485  \n",
       "1 -0.034896 -0.088456  0.001390  0.020588  0.057668  0.009820  \n",
       "2 -0.052116  0.012331  0.021749  0.002647 -0.094893  0.016705  \n",
       "3 -0.036212 -0.021916  0.034325 -0.011777 -0.036685  0.017355  \n",
       "4  0.066667  0.025039  0.100087  0.054306 -0.184202  0.027151  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_fraud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also can optionally visualize the generated synthetic data against the actual data using a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup parameters visualization parameters\n",
    "seed = 17\n",
    "test_size = 492 # number of fraud cases\n",
    "noise_dim = 32\n",
    "\n",
    "np.random.seed(seed)\n",
    "z = np.random.normal(size=(test_size, noise_dim))\n",
    "real = synthesizer.get_data_batch(train=train_sample, batch_size=test_size, seed=seed)\n",
    "real_samples = pd.DataFrame(real, columns=data_cols+label_cols)\n",
    "labels = fraud_w_classes['Class']\n",
    "\n",
    "model_names = ['GAN']\n",
    "colors = ['deepskyblue','blue']\n",
    "markers = ['o','^']\n",
    "class_labels = ['Class 1','Class 2']\n",
    "\n",
    "col1, col2 = 'V17', 'V10'\n",
    "\n",
    "base_dir = 'cache/'\n",
    "\n",
    "# Actual fraud data visualization\n",
    "model_steps = [ 0, 100, 200]\n",
    "rows = len(model_steps)\n",
    "columns = 1 + len(models)\n",
    "\n",
    "axarr = [[]]*len(model_steps)\n",
    "\n",
    "fig = plt.figure(figsize=(14,rows*3))\n",
    "\n",
    "# Go through each of the 3 model_step values -> 0, 100, 200\n",
    "for model_step_ix, model_step in enumerate(model_steps):        \n",
    "    axarr[model_step_ix] = plt.subplot(rows, columns, model_step_ix*columns + 1)\n",
    "\n",
    "    # Plot 'Class 1' and 'Class 2' samples taken from the original data, in a random shuffled fashion\n",
    "    for group, color, marker, label in zip(real_samples.groupby('Class_1'), colors, markers, class_labels ):\n",
    "        plt.scatter( group[1][[col1]], group[1][[col2]], \n",
    "                         label=label, marker=marker, edgecolors=color, facecolors='none' )\n",
    "    \n",
    "    plt.title('Actual Fraud Data')\n",
    "    plt.ylabel(col2) # Only add y label to left plot\n",
    "    plt.xlabel(col1)\n",
    "    xlims, ylims = axarr[model_step_ix].get_xlim(), axarr[model_step_ix].get_ylim()\n",
    "    \n",
    "    if model_step_ix == 0: \n",
    "        legend = plt.legend()\n",
    "        legend.get_frame().set_facecolor('white')\n",
    "\n",
    "    # Go through all the GAN models listed in 'model_names' and defined in 'models'\n",
    "    for i, model_name in enumerate( model_names[:] ):\n",
    "\n",
    "        [model_name, with_class, generator_model] = models[model_name]\n",
    "\n",
    "        generator_model.load_weights( base_dir + '_generator_model_weights_step_'+str(model_step)+'.h5')\n",
    "\n",
    "        ax = plt.subplot(rows, columns, model_step_ix*columns + 1 + (i+1) )\n",
    "\n",
    "        if with_class:\n",
    "            g_z = generator_model.predict([z, labels])\n",
    "            gen_samples = pd.DataFrame(g_z, columns=data_cols+label_cols)\n",
    "            for group, color, marker, label in zip( gen_samples.groupby('Class_1'), colors, markers, class_labels ):\n",
    "                plt.scatter( group[1][[col1]], group[1][[col2]], \n",
    "                                 label=label, marker=marker, edgecolors=color, facecolors='none' )\n",
    "        else:\n",
    "            g_z = generator_model.predict(z)\n",
    "            gen_samples = pd.DataFrame(g_z, columns=data_cols+['label'])\n",
    "            gen_samples.to_csv('./data/Generated_sample.csv')\n",
    "            plt.scatter( gen_samples[[col1]], gen_samples[[col2]],\n",
    "                             label=class_labels[0], marker=markers[0], edgecolors=colors[0], facecolors='none' )\n",
    "        plt.title(model_name)   \n",
    "        plt.xlabel(col1)\n",
    "        ax.set_xlim(xlims), ax.set_ylim(ylims)\n",
    "\n",
    "plt.suptitle('Comparison of GAN outputs', size=16, fontweight='bold')\n",
    "plt.tight_layout(rect=[0.075,0,1,0.95])\n",
    "\n",
    "# Adding text labels for training steps\n",
    "vpositions = np.array([ i._position.bounds[1] for i in axarr ])\n",
    "vpositions += ((vpositions[0] - vpositions[1]) * 0.35 )\n",
    "for model_step_ix, model_step in enumerate( model_steps ):\n",
    "    fig.text( 0.05, vpositions[model_step_ix], 'training\\nstep\\n'+str(model_step), ha='center', va='center', size=12)\n",
    "\n",
    "if not os.path.exists(\"./img\"):\n",
    "    os.makedirs(\"./img\")\n",
    "plt.savefig('img/Comparison_of_GAN_outputs.png', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8: Inverse transform the data to obtain the original format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that the generated synthetic data is still on the transformed form and needs to be inverse-transformed to the original format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arunnt\\Miniconda3\\envs\\synth\\lib\\site-packages\\sklearn\\base.py:446: UserWarning: X does not have valid feature names, but PowerTransformer was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "synthetic_data = inverse_transform(synthetic_fraud,preprocessor )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V7</th>\n",
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       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90892.210938</td>\n",
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       "      <td>0.219038</td>\n",
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       "      <td>0.024854</td>\n",
       "      <td>-0.171526</td>\n",
       "      <td>0.017887</td>\n",
       "      <td>0.086508</td>\n",
       "      <td>-0.124982</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035176</td>\n",
       "      <td>-0.027605</td>\n",
       "      <td>0.008802</td>\n",
       "      <td>-0.013290</td>\n",
       "      <td>0.075231</td>\n",
       "      <td>0.006691</td>\n",
       "      <td>-0.054382</td>\n",
       "      <td>0.011940</td>\n",
       "      <td>-0.002109</td>\n",
       "      <td>19.850132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91160.078125</td>\n",
       "      <td>0.278046</td>\n",
       "      <td>0.102798</td>\n",
       "      <td>0.209108</td>\n",
       "      <td>-0.044689</td>\n",
       "      <td>-0.035106</td>\n",
       "      <td>-0.163478</td>\n",
       "      <td>0.053725</td>\n",
       "      <td>0.061579</td>\n",
       "      <td>-0.090636</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053148</td>\n",
       "      <td>-0.040601</td>\n",
       "      <td>-0.008149</td>\n",
       "      <td>-0.039121</td>\n",
       "      <td>0.083893</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>-0.084565</td>\n",
       "      <td>0.005882</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>23.191448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90343.726562</td>\n",
       "      <td>0.392732</td>\n",
       "      <td>0.198758</td>\n",
       "      <td>0.245832</td>\n",
       "      <td>0.159641</td>\n",
       "      <td>0.006378</td>\n",
       "      <td>-0.127572</td>\n",
       "      <td>-0.022030</td>\n",
       "      <td>0.101584</td>\n",
       "      <td>-0.126588</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018609</td>\n",
       "      <td>-0.022591</td>\n",
       "      <td>0.025349</td>\n",
       "      <td>-0.013803</td>\n",
       "      <td>0.089407</td>\n",
       "      <td>-0.006806</td>\n",
       "      <td>-0.038308</td>\n",
       "      <td>0.014012</td>\n",
       "      <td>-0.003001</td>\n",
       "      <td>17.822559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92951.187500</td>\n",
       "      <td>0.420300</td>\n",
       "      <td>0.182411</td>\n",
       "      <td>0.157024</td>\n",
       "      <td>0.088787</td>\n",
       "      <td>0.079302</td>\n",
       "      <td>-0.144119</td>\n",
       "      <td>0.014256</td>\n",
       "      <td>0.091671</td>\n",
       "      <td>-0.130646</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075082</td>\n",
       "      <td>-0.029856</td>\n",
       "      <td>-0.005410</td>\n",
       "      <td>-0.010332</td>\n",
       "      <td>0.066248</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>-0.054135</td>\n",
       "      <td>0.019030</td>\n",
       "      <td>-0.007656</td>\n",
       "      <td>19.706913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87179.703125</td>\n",
       "      <td>0.372109</td>\n",
       "      <td>0.317210</td>\n",
       "      <td>0.179406</td>\n",
       "      <td>0.130793</td>\n",
       "      <td>-0.070776</td>\n",
       "      <td>-0.156429</td>\n",
       "      <td>-0.170158</td>\n",
       "      <td>0.112002</td>\n",
       "      <td>-0.211234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.034202</td>\n",
       "      <td>-0.030137</td>\n",
       "      <td>0.073659</td>\n",
       "      <td>0.054488</td>\n",
       "      <td>-0.032405</td>\n",
       "      <td>0.045231</td>\n",
       "      <td>0.013694</td>\n",
       "      <td>15.272158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0  90892.210938  0.347208  0.193120  0.219038  0.030985  0.024854 -0.171526   \n",
       "1  91160.078125  0.278046  0.102798  0.209108 -0.044689 -0.035106 -0.163478   \n",
       "2  90343.726562  0.392732  0.198758  0.245832  0.159641  0.006378 -0.127572   \n",
       "3  92951.187500  0.420300  0.182411  0.157024  0.088787  0.079302 -0.144119   \n",
       "4  87179.703125  0.372109  0.317210  0.179406  0.130793 -0.070776 -0.156429   \n",
       "\n",
       "         V7        V8        V9  ...       V20       V21       V22       V23  \\\n",
       "0  0.017887  0.086508 -0.124982  ... -0.035176 -0.027605  0.008802 -0.013290   \n",
       "1  0.053725  0.061579 -0.090636  ... -0.053148 -0.040601 -0.008149 -0.039121   \n",
       "2 -0.022030  0.101584 -0.126588  ... -0.018609 -0.022591  0.025349 -0.013803   \n",
       "3  0.014256  0.091671 -0.130646  ... -0.075082 -0.029856 -0.005410 -0.010332   \n",
       "4 -0.170158  0.112002 -0.211234  ...  0.003515  0.011086  0.034202 -0.030137   \n",
       "\n",
       "        V24       V25       V26       V27       V28     Amount  \n",
       "0  0.075231  0.006691 -0.054382  0.011940 -0.002109  19.850132  \n",
       "1  0.083893  0.002123 -0.084565  0.005882  0.002793  23.191448  \n",
       "2  0.089407 -0.006806 -0.038308  0.014012 -0.003001  17.822559  \n",
       "3  0.066248  0.001441 -0.054135  0.019030 -0.007656  19.706913  \n",
       "4  0.073659  0.054488 -0.032405  0.045231  0.013694  15.272158  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Add back the class column\n",
    "synthetic_data['Class']= 1\n",
    "\n",
    "#Write the data for profiling\n",
    "synthetic_data.to_csv('./data/creditcard_fraud_synthetic.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 9: Create a new checkpoint to validate the synthetic data against the real data\n",
    "\n",
    "For the normal usage of Great Expectations, the best way to validate data is with a Checkpoint. Checkpoints bundle Batches of data with corresponding Expectation Suites for validation.\n",
    "\n",
    "From the terminal, run the following command\n",
    "```great_expectations --v3-api checkpoint new my_new_checkpoint```\n",
    "\n",
    "This will again open a Jupyter Notebook that will allow you to complete the configuration of our Checkpoint. Edit the data_asset_name to reference the data we want to validate as \"creditcard_fraud_synthetic.csv\" (the file we wrote in step 8). Ensure that the expectation_suite_name is identical to what we created in step 4.\n",
    "\n",
    "Once done, go ahead and execute all the cells in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 10: Evaluate the synthetic data using Data Docs\n",
    "\n",
    "If you've following alone, you would have created the new checkpoint to validate the synthetic data. The last final step is to uncomment the final cell of the checkpoint notebook and execute it.\n",
    "\n",
    "This will open up a HTML page titled Data Docs. We can inspect the Data Docs, for the most recent check point and see that the expectation has failed. By clicking on the checkpoint run, we get a detailed report of which expectations failed from which columns.\n",
    "\n",
    "Based on this input we can do either of these actions:\n",
    "\n",
    "- Go back to our synthesizer and tweak the parameters, optimize it to get better synthetic data\n",
    "- Go back to the expectation suite and edit few expectations that is not important (maybe for certain columns). Yes - the expectations are customizable and here's how you can do it https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_expectations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In this tutorial we have successfully demonstrated the use of ydata-synthetic alongside great expectations. A 10 step guide was presented starting from configuring a data context to evaluating the synthesized data using Data Docs. We believe an integration of the these two libraries can help data sciencetist unlock the power of synthetic data with data quality."
   ]
  }
 ],
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